{"product_id":"large-language-models-graph-rag-a-hands-on-guide-to-knowledge-graph-integration-with-llms-paperback","title":"Large Language Models Graph RAG: A Hands-On Guide to Knowledge Graph Integration with LLMs - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMorgan Devline\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eIn this comprehensive guide, discover how to seamlessly integrate Knowledge Graphs with Large Language Models (LLMs) to build smarter, context-aware AI systems.\u003c\/p\u003e\u003cp\u003eThis book takes you on a transformative journey, covering everything from the foundations of LLMs and knowledge graphs to advanced topics like multi-hop reasoning, graph neural networks, and real-world applications in healthcare, e-commerce, and beyond.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat You'll Learn: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eThe principles behind Graph RAG and why it's the future of AI workflows.\u003c\/li\u003e\n\u003cli\u003eHow to design and build effective Knowledge Graphs using tools like Neo4j, SPARQL, and RDFLib.\u003c\/li\u003e\n\u003cli\u003eBest practices for integrating retrieved graph data into LLMs to enhance contextual reasoning and output accuracy.\u003c\/li\u003e\n\u003cli\u003eAdvanced graph-based reasoning techniques, including temporal knowledge graphs and dynamic updates.\u003c\/li\u003e\n\u003cli\u003ePractical applications across industries, from personalized recommendations to scientific discovery.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eKey Features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHands-On Projects: Build real-world Graph RAG systems with step-by-step tutorials.\u003c\/li\u003e\n\u003cli\u003eCode Examples: Clear, well-documented Python code for graph creation, querying, and integration with LLMs.\u003c\/li\u003e\n\u003cli\u003eVisual Aids: Diagrams, flowcharts, and case studies to simplify complex concepts.\u003c\/li\u003e\n\u003cli\u003ePractice Problems: Reinforce your learning with challenges and solutions designed for practitioners.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eWho This Book Is For: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAI Developers and Researchers: Build smarter and more context-aware LLM applications.\u003c\/li\u003e\n\u003cli\u003eData Scientists: Leverage knowledge graphs for better insights and data-driven reasoning.\u003c\/li\u003e\n\u003cli\u003eTech Enthusiasts and Students: Gain a deep understanding of cutting-edge AI technologies.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eAs AI systems grow more complex, the ability to integrate structured knowledge into LLMs is critical. This book equips you with the knowledge and tools to master \u003cb\u003eGraph RAG\u003c\/b\u003e, empowering you to innovate and lead in the evolving AI landscape.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 266\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.56 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e December 12, 2024\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":43154225135679,"sku":"9798303536050","price":25.81,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/1EEl9UUO8U9798303536050.webp?v=1776954906","url":"https:\/\/dhlswag.com\/products\/large-language-models-graph-rag-a-hands-on-guide-to-knowledge-graph-integration-with-llms-paperback","provider":"BBB","version":"1.0","type":"link"}